Engine misfire detection systems are employed within internal combustion engines in order to reduce the likelihood of harmful emissions being discharged by the engine. Conventional engine misfire detection systems, assemblies, and methodologies typically receive and/or measure the angular velocity of the engine's crankshaft to derive angular acceleration values that are used to determine the occurrence of misfires within the cylinders of the engine. Particularly, engine misfire detection systems analyze the derived acceleration values to determine whether any "acceleration deficits" (i.e., less than normal or desirable acceleration values) are present, and compare these "acceleration deficit" values to predetermined deficit values, which are expected in the event of a misfire, to determine if a misfire has occurred. The predetermined deficit values typically vary based upon the specific cylinder that is firing, and upon the operating condition of the vehicle's engine (e.g., the engine speed and load).
For example and without limitation, because the crankshaft is not "perfectly stiff", it gives rise to torsional oscillations, which are manifested as additions or subtractions to/from the acceleration of the crankshaft. These torsional oscillations vary based upon the cylinder that is firing and the operating conditions of the engine. Under certain operating conditions, accelerations resulting from these torsional oscillations may exceed or equal the acceleration deficits caused by a misfire, thereby significantly and adversely effecting the accuracy of misfire detection.
Various attempts have been made to compensate for the effects of torsional oscillations. For example and without limitation, engine misfire detection systems and methods have used adaptive schemes and static neural networks to compensate for the effects of torsional oscillations, such as the systems described within U.S. Pat. No. 5,531,108 of Feldkamp et al., and U.S. Pat. No. 5,774,823 of James et al., which are each assigned to the assignee of the present invention and which are each fully and completely incorporated herein by reference.
Other prior engine misfire detection systems have implemented dynamic neural networks to compensate for the effects of torsional oscillations. In one type of misfire detection system, described in U.S. Pat. No. 5,699,253 of Puskorius et al., which is assigned to the present assignee, and which is fully and completely incorporated herein by reference, a dynamic or "recurrent" neural network is trained to convert observed acceleration values to values which are nearly representative of values sensed by an ideal sensor operating in the absence of torsional oscillations. In this type of system, the neural network acts as a "nonlinear filter" or as an "inverse model".
In another type of misfire detection system, described in U.S. Pat. No. 5,732,382 of Puskorius et al., which is assigned to the present assignee and which is fully and completely incorporated herein by reference, a dynamic or "recurrent" neural network is trained to "detect" or to directly determine whether a misfire has occurred. In this type of system, the neural network effectively and operatively combines the functions of an inverse model and a classifier. While these neural network-type systems have been proven to be more effective in compensating for the effects of torsional oscillations than other prior methods and/or systems, they suffer from several drawbacks.
For example and without limitation, because these prior neural network type systems are typically "trained" as part of a development process, they have a limited ability to handle variations that arise from vehicle-to-vehicle variability and the effects of vehicle aging. Particularly, because the "training" process is relatively complicated, time consuming and computationally intensive, it is not suited to be carried out "on-line" or during the normal use of a vehicle or an engine.
Additionally, known or predetermined output target values, which are required to train the neural networks, are typically not available during the normal use of the vehicle. During the development of the neural networks, output target values are made to be available and/or are artificially and precisely "synthesized" by the use of, for example and without limitation, ideal laboratory grade sensors and equipment, complex filtering techniques, and artificially induced engine misfires. During the normal operation of an engine or vehicle, these types of artificially "synthesized" target output values are not available. Hence, these systems cannot be adaptively trained "on-line".
Another drawback associated with these prior systems is that they do not take into account the effect that misfires have on future firing events. By failing to consider the accelerations and torsional oscillations that are generated by a misfire, classifications or determinations of firing events, which occur after a misfire, may be incorrect or inaccurate.
Applicant's invention addresses these drawbacks and provides a method for detecting misfire within an engine which automatically adapts to the effects of vehicle variability and aging, and which takes into account the effect that misfires have on future firing events.